from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

print_time_report()

Time report

Daal4py_kmeans_short: 0h 0m 1s

Daal4py_ridge: 0h 0m 2s

Kmeans_short: 0h 0m 2s

Daal4py_logisticregression: 0h 0m 4s

Daal4py_kmeans_tall: 0h 0m 8s

Ridge: 0h 0m 10s

Logisticregression: 0h 0m 20s

Kmeans_tall: 0h 0m 22s

Daal4py_kneighborsclassifier_kd_tree: 0h 0m 33s

Daal4py_kneighborsclassifier: 0h 2m 40s

Kneighborsclassifier_kd_tree: 0h 2m 54s

Xgboost: 0h 5m 1s

Lightgbm: 0h 5m 4s

Catboost_symmetric: 0h 5m 7s

Histgradientboostingclassifier: 0h 5m 18s

Catboost_lossguide: 0h 5m 24s

Kneighborsclassifier: 0h 34m 19s

Total: 1h 7m 38s

print_env_info()

Benchmark environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.142	0.000	5.638	0.000	1	5	NaN	NaN	0.504	0.000	0.281	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	19.632	0.552	0.000	0.020	1	5	0.819	0.710	1.796	0.012	10.930	0.316	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.194	0.001	0.000	0.194	1	5	1.000	0.000	0.092	0.001	2.094	0.026	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.134	0.000	5.992	0.000	1	100	NaN	NaN	0.494	0.000	0.270	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	19.767	0.061	0.000	0.020	1	100	0.941	0.931	1.967	0.055	10.049	0.283	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.195	0.001	0.000	0.195	1	100	1.000	1.000	0.093	0.001	2.091	0.025	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.139	0.000	5.745	0.000	-1	5	NaN	NaN	0.492	0.000	0.283	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	33.359	0.000	0.000	0.033	-1	5	0.819	0.710	1.882	0.045	17.724	0.424	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.182	0.013	0.000	0.182	-1	5	1.000	0.000	0.096	0.002	1.891	0.147	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.137	0.000	5.822	0.000	-1	1	NaN	NaN	0.497	0.000	0.277	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	27.403	0.207	0.000	0.027	-1	1	0.715	0.808	2.007	0.036	13.654	0.266	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.174	0.013	0.000	0.174	-1	1	1.000	1.000	0.096	0.001	1.816	0.140	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.136	0.000	5.898	0.000	1	1	NaN	NaN	0.488	0.000	0.278	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	13.567	0.076	0.000	0.014	1	1	0.715	0.931	2.007	0.037	6.758	0.129	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.195	0.001	0.000	0.195	1	1	1.000	1.000	0.096	0.001	2.036	0.030	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.132	0.000	6.052	0.000	-1	100	NaN	NaN	0.491	0.000	0.269	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	33.726	0.000	0.000	0.034	-1	100	0.941	0.808	1.903	0.031	17.726	0.292	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.177	0.013	0.000	0.177	-1	100	1.000	1.000	0.100	0.010	1.770	0.224	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.283	0.000	1	5	NaN	NaN	0.098	0.000	0.575	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.321	0.074	0.000	0.021	1	5	0.989	0.972	0.289	0.005	73.692	1.258	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.027	0.000	0.000	0.027	1	5	1.000	1.000	0.006	0.000	4.613	0.256	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.057	0.000	0.282	0.000	1	100	NaN	NaN	0.097	0.000	0.587	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	21.586	0.068	0.000	0.022	1	100	0.987	0.978	0.331	0.007	65.311	1.434	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.029	0.001	0.000	0.029	1	100	1.000	1.000	0.006	0.000	4.832	0.262	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.060	0.000	0.266	0.000	-1	5	NaN	NaN	0.097	0.000	0.617	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	35.431	0.000	0.000	0.035	-1	5	0.989	0.972	0.272	0.004	130.353	1.852	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.032	0.001	0.000	0.032	-1	5	1.000	1.000	0.006	0.000	5.541	0.274	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.062	0.000	0.259	0.000	-1	1	NaN	NaN	0.097	0.000	0.637	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	24.732	0.094	0.000	0.025	-1	1	0.978	0.979	0.276	0.007	89.549	2.377	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.001	0.000	0.020	-1	1	1.000	1.000	0.006	0.000	3.452	0.241	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.059	0.000	0.269	0.000	1	1	NaN	NaN	0.098	0.000	0.609	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.946	0.018	0.000	0.011	1	1	0.978	0.978	0.320	0.007	34.191	0.795	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.016	0.000	0.000	0.016	1	1	1.000	1.000	0.006	0.000	2.716	0.093	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.059	0.000	0.272	0.000	-1	100	NaN	NaN	0.097	0.000	0.607	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	35.452	0.000	0.000	0.035	-1	100	0.987	0.979	0.262	0.004	135.515	1.888	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.033	0.002	0.000	0.033	-1	100	1.000	1.000	0.006	0.000	5.752	0.489	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.576	0.000	0.022	0.000	-1	100	NaN	NaN	0.802	0.000	4.460	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	3.024	0.021	0.000	0.003	-1	100	0.967	0.958	0.125	0.001	24.159	0.300	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.006	0.000	0.000	0.006	-1	100	1.000	1.000	0.000	0.000	20.063	11.362	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.651	0.000	0.022	0.000	-1	1	NaN	NaN	0.792	0.000	4.610	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.489	0.006	0.000	0.000	-1	1	0.957	0.971	0.718	0.013	0.681	0.014	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.001	0.000	3.729	1.583	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.531	0.000	0.023	0.000	1	1	NaN	NaN	0.786	0.000	4.492	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.819	0.011	0.000	0.001	1	1	0.957	0.970	0.236	0.003	3.477	0.060	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	2.157	1.227	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.432	0.000	0.023	0.000	1	5	NaN	NaN	0.771	0.000	4.453	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.594	0.044	0.000	0.002	1	5	0.967	0.958	0.130	0.011	12.299	1.069	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.369	2.581	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.948	0.000	0.020	0.000	1	100	NaN	NaN	0.774	0.000	5.101	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	5.269	0.017	0.000	0.005	1	100	0.967	0.971	0.700	0.010	7.530	0.110	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.000	0.000	0.004	1	100	1.000	1.000	0.001	0.000	5.146	2.181	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.418	0.000	0.023	0.000	-1	5	NaN	NaN	0.783	0.000	4.367	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.894	0.015	0.000	0.001	-1	5	0.967	0.970	0.233	0.003	3.834	0.079	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	7.585	3.823	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.830	0.000	0.019	0.000	-1	100	NaN	NaN	0.484	0.000	1.714	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.044	0.001	0.000	0.000	-1	100	0.986	0.981	0.001	0.000	57.358	23.614	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	21.371	18.208	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.889	0.000	0.018	0.000	-1	1	NaN	NaN	0.475	0.000	1.874	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.000	0.001	0.000	-1	1	0.971	0.988	0.007	0.000	3.688	0.247	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.248	15.882	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.883	0.000	0.018	0.000	1	1	NaN	NaN	0.476	0.000	1.856	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.024	0.000	0.001	0.000	1	1	0.971	0.988	0.001	0.000	22.562	8.172	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.888	5.602	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.865	0.000	0.018	0.000	1	5	NaN	NaN	0.472	0.000	1.832	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.027	0.000	0.001	0.000	1	5	0.982	0.981	0.001	0.000	35.385	14.653	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.881	5.113	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.914	0.000	0.018	0.000	1	100	NaN	NaN	0.483	0.000	1.894	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.058	0.000	0.000	0.000	1	100	0.986	0.988	0.007	0.001	8.510	0.767	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.372	4.569	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.883	0.000	0.018	0.000	-1	5	NaN	NaN	0.474	0.000	1.864	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.001	0.001	0.000	-1	5	0.982	0.988	0.001	0.000	25.404	8.378	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	23.831	21.778	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.589	0.0	0.815	0.000	random	NaN	30	NaN	0.433	0.0	1.360	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.399	0.000	random	0.001	30	0.001	0.000	0.0	7.557	4.723	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.141	7.918	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.574	0.0	0.837	0.000	k-means++	NaN	30	NaN	0.378	0.0	1.517	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.403	0.000	k-means++	0.001	30	0.001	0.000	0.0	7.604	4.685	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.885	8.459	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.643	0.0	3.613	0.000	random	NaN	30	NaN	3.179	0.0	2.090	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.230	0.000	random	0.002	30	0.002	0.000	0.0	5.586	2.608	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	random	1.000	30	1.000	0.000	0.0	9.041	5.945	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.587	0.0	3.644	0.000	k-means++	NaN	30	NaN	2.997	0.0	2.198	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.300	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.617	2.447	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.489	6.999	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.234	0.0	0.014	0.000	k-means++	NaN	20	NaN	0.087	0.0	2.671	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.185	0.000	k-means++	-0.000	20	0.001	0.001	0.0	2.603	0.618	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.163	7.875	See	See
3	KMeans_short	fit	10000	10000	2	20	0.077	0.0	0.041	0.000	random	NaN	20	NaN	0.033	0.0	2.353	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.189	0.000	random	0.001	20	0.000	0.001	0.0	2.471	0.605	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	8.800	5.888	See	See
6	KMeans_short	fit	10000	10000	100	20	0.602	0.0	0.266	0.000	k-means++	NaN	20	NaN	0.355	0.0	1.697	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.681	0.000	k-means++	0.306	20	0.338	0.001	0.0	1.974	0.335	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	6.532	3.309	See	See
9	KMeans_short	fit	10000	10000	100	20	0.225	0.0	0.713	0.000	random	NaN	20	NaN	0.153	0.0	1.463	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.899	0.000	random	0.342	20	0.326	0.001	0.0	1.873	0.368	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.013	0.001	random	1.000	20	1.000	0.000	0.0	6.617	3.790	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.460	0.0	[-0.1029581]	0.000	NaN	NaN	NaN	NaN	NaN	2.111	0.0	5.430	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[54.53203843]	0.000	NaN	NaN	NaN	NaN	0.544	0.000	0.0	0.819	0.424	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.20330007]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.423	0.376	See	See
3	LogisticRegression	fit	1000	1000	10000	[28]	0.885	0.0	[-2.32204547]	0.001	NaN	NaN	NaN	NaN	NaN	0.843	0.0	1.050	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[28]	0.002	0.0	[133.85272173]	0.000	NaN	NaN	NaN	NaN	0.210	0.003	0.0	0.591	0.142	See	See
5	LogisticRegression	predict	1000	1	10000	[28]	0.000	0.0	[20.51244484]	0.000	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.164	0.111	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.190	0.0	0.421	0.0	NaN	NaN	NaN	0.202	0.0	0.941	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.012	0.0	6.499	0.0	NaN	NaN	0.111	0.021	0.0	0.587	0.008	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	0.649	0.0	NaN	NaN	NaN	0.000	0.0	1.023	1.914	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.473	0.0	0.543	0.0	NaN	NaN	NaN	0.250	0.0	5.887	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	4.646	0.0	NaN	NaN	1.000	0.000	0.0	0.725	0.567	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.013	0.0	NaN	NaN	NaN	0.000	0.0	0.627	0.664	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw results

Smoothed HPO Curves